Rutav Shah
I am a third year Computer Science PhD student at UT Austin, co-advised by Prof. Yuke Zhu at the RPL lab and Prof. Roberto Martín-Martín at the RobIn lab. My research interests span the fields of Robotics and Machine Learning.
For my undergraduate thesis, I was co-advised by Prof. Abir Das (Computer Vision and Intelligence Research Lab at IIT Kharagpur) and Dr. Vikash Kumar (FAIR Pittsburgh).
Apart from research, I love teaching, trekking, and running! Some high altitude treks I have completed, Tapovan, Brahmatal in the Garhwal region and Buran Pass in the Himachal Himalayas.
Be kind, cheers!
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Research
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* Equal contribution    † Equal advising |
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BUMBLE: Unifying Reasoning and Acting with VLMs for Building-Wide Mobile Manipulation
Rutav Shah, Albert Yu, Yifeng Zhu, Yuke Zhu†, Roberto Martín-Martín†
Technical Report, Oct. 2024
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arXiv
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LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery
Weikang Wan, Yifeng Zhu*, Rutav Shah*, Yuke Zhu
International Conference on Robotics and Automation (ICRA), 2024
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arXiv
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment Collaboration
International Conference on Robotics and Automation (ICRA), 2024
Best Conference Paper Award
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arXiv
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MUTEX: Learning Unified Policies from Multimodal Task Specifications
Rutav Shah,
Roberto Martín-Martín†,
Yuke Zhu†
Conference on Robot Learning (CoRL), 2023
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arXiv
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RoboHive: A Unified Framework for Robot Learning
Vikash Kumar, Rutav Shah*, Gaoyue Zhou*, Vincent Moens, Vittorio Caggiano, Jay Vakil, Abhishek Gupta, Aravind Rajeswaran
Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2023
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arXiv
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Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das
Conference on Neural Information Processing Systems (NeurIPS), 2021.
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arXiv
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RRL: Resnet as Representation for Reinforcement Learning
Rutav Shah*, Vikash Kumar*
International Conference on Machine Learning (ICML), 2021.
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video /
arXiv
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